The target-speaker voice activity detection (TS-VAD) system was proposed recently [1]}, and demonstrated promising results in the CHiME-6 challenge [2]}. The model was inspired from advances in target speech extraction methods (such as SpeakerBeam [3]}, VoiceFilter [4]}, and Personal-VAD [5]}), which utilize the target speakers' enrollment information to estimate time-frequency masks that indicate their voice activity in the recording. Inspired from supervised diarization methods like EEND, the network generates multi-speaker outputs containing frame-wise posteriors by leveraging the conditional dependence of speakers in a recording. TS-VAD achieved the best diarization performance on CHiME-6 (which contains 34% overlap duration in the evaluation set), improving the baseline agglomerative hierarchical clustering (AHC) system by over 30% absolute diarization error rate (DER).
